# Deep Learning-Guided Image Reconstruction from Incomplete Data

arXiv: https://arxiv.org/abs/1709.00584 · HTML: https://mlsanity.com/paper/1709.00584/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems](https://mlsanity.com/paper/2202.05311/) (2022)
- [Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz](https://mlsanity.com/paper/2305.07618/) (2023)
- [Survey of Deep Learning Methods for Inverse Problems](https://mlsanity.com/paper/2111.04731/) (2021)
- [Sinogram Enhancement with Generative Adversarial Networks using Shape Priors](https://mlsanity.com/paper/2202.00419/) (2022)
- [HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning](https://mlsanity.com/paper/2304.14412/) (2023)
- [Hallucinations in medical devices](https://mlsanity.com/paper/2508.14118/) (2025)

## Abstract (arXiv)

An approach to incorporate deep learning within an iterative image reconstruction framework to reconstruct images from severely incomplete measurement data is presented. Specifically, we utilize a convolutional neural network (CNN) as a quasi-projection operator within a least squares minimization procedure. The CNN is trained to encode high level information about the class of images being imaged; this information is utilized to mitigate artifacts in intermediate images produced by use of an iterative method. The structure of the method was inspired by the proximal gradient descent method, where the proximal operator is replaced by a deep CNN and the gradient descent step is generalized by use of a linear reconstruction operator. It is demonstrated that this approach improves image quality for several cases of limited-view image reconstruction and that using a CNN in an iterative method increases performance compared to conventional image reconstruction approaches. We test our method on several limited-view image reconstruction problems. Qualitative and quantitative results demonstrate state-of-the-art performance.